The present invention relates to a data processing system for calculating a comparison value to be compared with observation data and particularly to a data processing system for calculating a comparison value based on past observation data.
In recent years, a stream mining technique for analyzing useful rules, useful patterns and the like from time-series data in real time has attracted attention as the amount of data to be handled has become huge. For example, by the application of the stream mining technique to the monitoring of an IT system, a behavior of the IT system different from a normal one can be detected. Since this enables a sign of a silent failure to be detected, this silent failure can be dealt with before occurring.
Conventionally, a failure detection technique (abnormality determination technique) in IT systems is for determining that a current value is an abnormal value different from a normal value when the current value deviates from a reference value (baseline), which is a value in normal time, by a predetermined value (threshold value) or more.
A method for automatically setting a threshold value from a periodical tendency of past data of periodic time-series data (e.g. data of network traffic or the like) is known as a method for setting a threshold value for highly accurate and efficient execution of abnormality determination (see, for example, patent literature 1).
In a method disclosed in patent literature 1, a statistic (average value and standard deviation) of time-series data is calculated based on a period, a representative time (sampling time), a consideration period (data width for calculating the average value and standard deviation) for the representative time included in configuration definition information set by a user, and the threshold value is automatically set based on the calculated statistic.    Patent Literature 1: JP2008-311719A